Impact of Atypical Antipsychotic Use Among Adolescents With Attention-Deficit/Hyperactivity Disorder

A retrospective study of the treatment patterns and economic outcomes associated with off-label atypical antipsychotic use in the treatment of adolescents with attention-deficit/hyperactivity disorder.

Objectives
To compare treatment patterns, resource utilization, and costs to US third-party payers of stimulant-treated adolescent attentiondeficit/ hyperactivity disorder (ADHD) patients who switched to or augmented with atypical antipsychotics (AAPs; not FDA-indicated for ADHD) with those who switched to or augmented with nonantipsychotic medications.

Methods
Adolescent patients with an ADHD diagnosis and ≥1 stimulant medication claim between January 2005 and December 2009 were identified. Patients were classified into the AAP or non-antipsychotic cohorts based on subsequent claims for AAPs or nonantipsychotic medications, respectively. Patients with psychiatric diagnoses for which AAPs are often prescribed were excluded. Patients were matched 1:1 from the AAP to the non-antipsychotic cohort using propensity score matching. Treatment patterns, resource utilization, and costs in the 12 months after AAP or non-antipsychotic initiation were compared using Cox models, Poisson regression, and Wilcoxon signed-rank tests, respectively.

Conclusions
Stimulant-treated adolescents with ADHD who switched to or augmented with AAPs had significantly greater drug augmentation, healthcare resource utilization, and costs compared with the non-antipsychotic cohort.

Am J Manag Care. 2014;20(9):711-721

• This study was the first to examine the treatment patterns and economic outcomes associated with off-label atypical antipsychotic (AAP) use in the treatment of adolescent attention-deficit/hyperactivity disorder using real-world data.

Atypical antipsychotics (AAPs) are one of the most common and costly classes of prescription drugs, with annual expenditures exceeding $13 billion, representing nearly 5% of all drug expenditures in the US.1,2 AAPs are approved by the FDA for the treatment of schizophrenia, behavioral symptoms in autism, and mixed or manic bipolar disorder, and the benefits and risks of AAPs are well documented for these indications.3 However, AAP use for off-label indications has rapidly increased, and now accounts for the majority of AAP utilization.4,5 A recent study found that AAP use in children grew by 62% from 2002 to 2007.6 Due to the potential side effects of AAP use and limited clinical evidence regarding the efficacy and safety of such off-label uses, the utilization of atypical antipsychotics for off-label indications is controversial.5,7-9 Among studies of off-label AAP use, heightened attention has been paid to attention-deficit/hyperactivity disorder (ADHD), as almost a third of off-label AAP use is related to this condition.7-9

ADHD can pose a significant barrier to personal development and cause substantial psychological difficulties for patients and their families if left untreated.10 There are many pharmacologic treatment options for ADHD, including stimulants and non-stimulants, which have well-established efficacy and safety profiles.11 Conversely, the risks and benefits of AAP use in current clinical practice for ADHD are largely unknown.12 The few clinical studies that investigated AAP use in ADHD patients are confounded by patient comorbidities for which AAPs may be appropriate, and are therefore difficult to interpret.13-16 However, the pediatric population appears to be at higher risk than adults for AAPinduced adverse events including weight gain, elevation in prolactin levels, extrapyramidal symptoms, sedation, and cardiac events.17-19 Additionally, there is limited evidence regarding the real-world economic outcomes of AAP use for ADHD, and recent literature has called for further investigation of the health outcomes in pediatric populations.6 A companion study examined this effect in children aged 6 to 12 years with ADHD, finding that patients who utilized AAPs had higher rates of drug switching and augmentation, greater medical resource utilization, and higher total healthcare costs compared with patients who used nonantipsychotic therapies.20 However, no peer-reviewed publications have investigated the economic costs of AAP use specifically in adolescents with ADHD. There is evidence that both the rates of ADHD diagnosis and AAP prescription for ADHD can vary by age category, which indicates the need for specific attention to the adolescent subpopulation.5,7,9,21

Therefore, the purpose of this study was to compare, from a US third-party payer perspective, treatment patterns, resource utilization, and healthcare costs of stimulant- treated adolescents with ADHD who switched to or augmented their stimulant treatment with AAPs, versus those who did the same with non-antipsychotic medications.

METHODS

Data

This retrospective cohort study was conducted utilizing data from the Truven Health MarketScan Commercial Claims and Encounters database. These data include commercial health insurance claims and enrollment data from large employers and health plans across the United States. Such plans provide private healthcare coverage for more than 45 million employees and their spouses and dependents. This administrative claims database includes a variety of fee-for-service, preferred provider organization, and capitated health plans.

Sample Selection

Patients were required to have at least 1 medical claim associated with a primary diagnosis of ADHD (InternationalClassification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] codes 314.00 and 314.01) and 1 pharmacy claim for a stimulant (eAppendix available at www.ajmc.com) during the period of January 1, 2005, to December 31, 2009, to be included in the study sample. The date of the first stimulant claim during this period was defined as the initial stimulant date.

Patients with a pharmacy claim for an AAP (Appendix) after the initial stimulant date were selected into the AAP cohort. The date of the first AAP pharmacy claim after the initial stimulant date was defined as the index date, and the AAP filled was defined as the index drug. Patients with no pharmacy claims for AAPs after the initial stimulant date, but at least 1 claim for atomoxetine, guanfacine immediaterelease (IR), clonidine IR, or a stimulant of a different class than the initial stimulant were selected into the non-antipsychotic cohort. For patients who had pharmacy claims for more than 1 non-antipsychotic medication after the initial stimulant date (eg, having claims for both atomoxetine and clonidine IR after the initial stimulant), the index drug was randomly selected from the eligible non-antipsychotic medications. This random selection method was employed to match the treatment history of the AAP cohort (which may have one or more non-antipsychotic medications prior to the index date) in an unbiased manner.

Patients in both cohorts were required to be aged 13 to 17 years on the index date. Individuals were included in the study if they had at least 30 days of supply of a stimulant before the index date and at least 18 months (6 months pre- and 12 months post index date) of continuous eligibility for their health plan. Furthermore, patients were required to have at least one primary diagnosis of ADHD during this 18-month period. Patients were only included if they switched from the initial stimulant to the index drug or augmented a stimulant with the index drug. Patients were considered to have switched if they initiated the index drug within a period defined as beginning 30 days before and ending 30 days after the last day with stimulant supply. Patients were considered to have augmented with the index drug if they had at least 30 consecutive days of overlap between the stimulant and the index drug.

To increase the likelihood that patients’ pharmacy claims were for the treatment of ADHD and not for other comorbid conditions that are often treated with AAPs, patients were excluded if they had a medical claim associated with any of the following diagnoses during the 18-month study period: bipolar disorder, delusions/hallucinations, paranoia, psychosis, tics, or dementia. These conditions were identified by group of medical experts as indications for which AAPs are approved by the FDA or commonly prescribed.

To control for observable confounding factors, propensity score matching was used to match patients in the non-antipsychotic cohort 1-to-1 with patients in the AAP cohort. The propensity score was estimated using an unconditional logistic regression including patient characteristics during the 6-month pre-index period. In addition, patients were exactly matched on whether the patient switched to or augmented with the index drug.

Outcomes

All outcomes were measured during the 12-month period following the index date. Outcome categories included treatment patterns, healthcare utilization, and costs.

Treatment pattern outcomes included discontinuation, switching, and augmentation. Discontinuation was defined as a gap in the usage of the index therapy greater than 30 days. A switch was defined as the initiation of a new ADHD medication (either an AAP or a non-antipsychotic other than the index therapy and the current stimulant) within 30 days before or after the index therapy discontinuation date. Augmentation was defined as initiation of a new ADHD medication, in which the supply of the newly initiated medication had at least 30 days of supply overlap with the index therapy.

Healthcare utilization outcomes included 3 mutually exclusive categories: inpatient, emergency department (ED), and outpatient services. In each category, both all-cause utilization and mental health (MH)-related utilization (ICD-9-CM: codes 290.xx-319.xx) were calculated. Healthcare costs included medical costs and prescription drug costs. Within each category, both all-cause and MH-related costs were evaluated. Cost analyses were conducted from a third-party payer perspective, where costs were defined as the total amount paid without including out-of-pocket costs to patients. All costs were inflated to 2010 US dollars using the medical component of the Consumer Price Index.22

Statistical Analysis

During the 6-month pre-index baseline period, baseline characteristics of patients in the AAP cohort were compared with those of the non-antipsychotic cohort before and after propensity score matching. Comparisons employed c2 tests and Wilcoxon rank-sum tests before matching, and McNemar’s tests and Wilcoxon signed-rank tests after matching.

Treatment patterns were estimated using the Kaplan- Meier (KM) survival estimator; the corresponding hazard ratios (HRs) were estimated using Cox proportional hazard models. Rates of all-cause and MH-related medical utilization were compared between patients in the 2 matched cohorts using McNemar’s test, and the corresponding incidence rate ratios (IRRs) were estimated using Poisson regression. Since cost outcomes are typically not normally distributed and highly skewed to the right, cost comparisons were conducted using Wilcoxon signed-rank tests.

All analyses were performed using SAS Version 9.2 (SAS Institute, Cary, North Carolina), and statistical significance was evaluated at the .05 level (2-sided).

RESULTS Between January 1, 2005, and December 31, 2009, a total of 263,772 adolescents with at least 1 primary diagnosis of ADHD were identified from the database (Figure 1). Of these, 156,291 (59.3%) filled at least 1 prescription for a stimulant medication. Among those with at least 1 stimulant prescription, 17,268 (11.0%) had at least 1 prescription for an AAP medication after the initial stimulant. Similarly, 51,333 (32.8%) had at least 1 prescription for a non-antipsychotic medication (and no fills for an AAP medication) after the initial stimulant date. Among the AAP cohort, 9511 (55.1%) patients did not have any of the psychiatric diagnoses for which AAPs are commonly used. A total of 1151 patients in the AAP cohort and 6528 patients in the non-antipsychotic cohort met all additional eligibility criteria and were included in the study. A total of 1698 patients were included after matching, with 849 patients (187 who switched to the index drug, and 662 who augmented with it) included in each cohort. Inspection of the matched pairs within the propensity score histogram demonstrated that there was sufficient numeric representation and appropriate overlap between the two cohorts across the full range of propensity scores.